def main():
opt = BaseOptions().parse()
if opt.test_type == 'video' or opt.test_type == 'image':
import Test_Gen_Models.Test_Video_Model as Gen_Model
from Dataloader.Test_load_video import Test_VideoFolder
elif opt.test_type == 'audio':
import Test_Gen_Models.Test_Audio_Model as Gen_Model
from Dataloader.Test_load_audio import Test_VideoFolder
else:
raise ('test type select error')
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.sequence_length = 1
test_nums = [1, 2, 3, 4] # choose input identity images
model = Gen_Model.GenModel(opt)
# _, _, start_epoch = util.load_test_checkpoint(opt.test_resume_path, model)
start_epoch = opt.start_epoch
visualizer = Visualizer(opt)
# find the checkpoint's path name without the 'checkpoint.pth.tar'
path_name = ntpath.basename(opt.test_resume_path)[:-19]
web_dir = os.path.join(opt.results_dir, path_name,
'%s_%s' % ('test', start_epoch))
for i in test_nums:
A_path = os.path.join(opt.test_A_path, 'test_sample' + str(i) + '.jpg')
test_folder = Test_VideoFolder(root=opt.test_root,
A_path=A_path,
config=opt)
test_dataloader = DataLoader(test_folder,
batch_size=1,
shuffle=False,
num_workers=1)
model, _, start_epoch = util.load_test_checkpoint(
opt.test_resume_path, model)
# inference during test
for i2, data in enumerate(test_dataloader):
if i2 < 5:
model.set_test_input(data)
model.test_train()
# test
start = time.time()
for i3, data in enumerate(test_dataloader):
model.set_test_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
visualizer.save_images_test(web_dir, visuals, img_path, i3,
opt.test_num)
end = time.time()
print('finish processing in %03f seconds' % (end - start))
from torch.utils.data import DataLoader
from Dataloader.Test_load_audio import Test_VideoFolder
import os
from Options_all import BaseOptions
import matlab.engine
matlab = matlab.engine.start_matlab()
wf = wave.open('0572_0019_0003.wav', 'rb')
wav = wf.readframes(16000 * 10)
wav = wav[1::2]
wav = np.fromstring(wav, 'Int16')
wav = wav
opt = BaseOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.sequence_length = 1
A_path = os.path.join(opt.test_A_path, 'test_sample' + str(3) + '.jpg')
test_folder = Test_VideoFolder(root='./0572_0019_0003',
A_path=A_path,
config=opt)
test_dataloader = DataLoader(test_folder, batch_size=1)
enum = list(enumerate(test_dataloader))
mfcc_bin = enum[0][1]['B_audio'].numpy()[0][0][0]
mfcc_feat = mfcc(wav[0:10000],
16000,
winlen=0.025,
winstep=0.01,
from torch.utils.data import DataLoader
import os
import ntpath
opt = BaseOptions().parse()
if opt.test_type == 'video' or 'image':
import Test_Gen_Models.Test_Video_Model as Gen_Model
from Dataloader.Test_load_video import Test_VideoFolder
elif opt.test_type == 'audio':
import Test_Gen_Models.Test_Audio_Model as Gen_Model
from Dataloader.Test_load_audio import Test_VideoFolder
else:
raise('test type select error')
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.sequence_length = 1
test_nums = [1, 2, 3, 4] # choose input identity images
model = Gen_Model.GenModel(opt)
# _, _, start_epoch = util.load_test_checkpoint(opt.test_resume_path, model)
start_epoch = opt.start_epoch
visualizer = Visualizer(opt)
# find the checkpoint's path name without the 'checkpoint.pth.tar'
path_name = ntpath.basename(opt.test_resume_path)[:-19]
web_dir = os.path.join(opt.results_dir, path_name, '%s_%s' % ('test', start_epoch))
for i in test_nums:
A_path = os.path.join(opt.test_A_path, '/test_sample' + str(i) + '.jpg')
test_folder = Test_VideoFolder(root=opt.test_root, A_path=A_path, config=opt)
test_dataloader = DataLoader(test_folder, batch_size=1,
shuffle=False, num_workers=1)